How to build a data-driven sales team to turbocharge growth

Modern B2B sales teams combine data analysis with strategic relationship-building to drive consistent growth. Data guides how they identify and prioritize high-potential leads, craft outreach messaging, forecast future performance, allocate resources, and track performance. The outcome? Higher win rates and increased revenue.

These benefits are why many modern companies are focusing on building data-driven sales teams. “Sales and revenue operations — responsible for managing and analyzing data — are more important than ever for any software company,” says Tony Rodoni, Operating Partner at Bessemer. [At Bessemer, Tony facilitates a ​​community of go-to-market (GTM) leaders and mentors portfolio company leaders on GTM strategy—an area in which he developed expertise following 15 years at Salesforce, where he was an executive vice president and led sales, sales enablement, and customer experience teams.] However, GTM leaders often struggle with operationalizing it, so Tony talked to the few who have succeeded.

He sat down with Ross Biestman, CRO at ServiceTitan, and Lekha Doshi, VP of GTM Operations at LinkedIn to discuss strategies for building a data-driven sales team. In this playbook, we distill their insights on how to create a data strategy, key metrics to track, and practical strategies to apply to transform your sales and go-to-market (GTM) processes.

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Five key steps to building a data-driven sales organization

Sales is both an art and a science, explains Ross Biestman, Chief Revenue Officer at ServiceTitan. The art focuses on human connection, empathy, and adaptability, while the science leverages data, processes, and systems to ensure consistent, scalable outcomes.

Neither side outweighs the other, and sales success requires a balance of both. “For any go-to-market team or founder looking to accelerate their growth engine, you need to strike that balance between the art and science of sales,” says Ross.

With this in mind, here are some of the next steps to building a data-driven sales operation.

Start collecting data now 

Begin gathering data as soon as you can about your customers, prospects, and sales activities. Do this even if your systems aren't fully mature or you don’t have a team yet. The earlier you start, the more insights you'll have to work with when you’re ready. “If you're going to use data to make decisions, you need to start a history of data,” Tony Rodoni, Operating Partner at Bessemer, says.

While it’s ideal to collect data with a clear view of what you need and how you’ll use it, in our experience, it’s still best to gather it early. To strengthen data you collect on your customer relationship management (CRM) platform, it can help to search databases like ZoomInfo or Dun & Bradstreet, alongside industry association databases, for prospects that meet your total addressable market (TAM) constraints. (Learn more in our SDR guide today.) You can then filter and refine it to extract meaningful insights when you’re ready.

Hire a data-curious and data-driven individual 

Hiring the right data-driven person is essential. “The right person can extract, analyze, and generate actionable insights from data,” says Lekha Doshi, VP of GTM operations at LinkedIn. It’s especially important for GTM operations leaders and early hires to be able to translate board- and executive-level goals and on-the-ground data into attainable yet ambitious targets. (Learn more in our GTM Ops 101 guide.)

This person should be both data-curious and data-driven, as their role is to create the systems and processes that support and scale the sales team. They should also already have some experience in this capacity and be eager to figure things out, find patterns, and match them.

Ross shares their first sales ops leader handled everything from creating dashboards, managing forecasts, planning territories and accounts, and even logistical tasks like preparing contracts or attending industry events. “It comes down to making that first great hire who can build a team to put structure around your GTM motion and serve as a valuable partner to leaders,” he says.

Build a solid data foundation

Realizing the full benefits of being a data-driven team will depend on the quality of your data. Garbage data can give you the wrong insights or lead to bad decisions. That’s why it’s important for your data to meet the six quality dimensions: accuracy, completeness, consistency, validity, uniqueness and integrity.

To build a solid data foundation, use a single source of truth, such as a CRM, to consolidate data from multiple sources. Additionally, collaborate with other departments to ensure clean data collection. “I often partner with our R&D organization—I’m constantly working with data science and our engineering team to ensure we gather the data we need in a clean, usable way,” shares Lekha.

Identify your sellable, obtainable market

To build an effective GTM operation, start by understanding your product-market fit — how your product meets customer needs. Then, identify your ideal customer profile and sellable, obtainable market. Do this by analyzing your best customers and comparing them to those who didn’t succeed or deals you lost.

Ross shares a lesson from early in his career when he initially thought he could sell to every company he saw on billboards. Over time, he realized that while he could sell to everyone, it wasn’t a wise approach. "What I realized as I spent more time in the business is that being really precise about what it means to go after your ideal customer profile is the foundation for building a go-to-market engine that’s repeatable."

Solve a problem, save the data, scale it

You don’t need a sophisticated system to start. Focus on solving practical problems that match your company’s stage, then save and build on the data. “You have to build the version for the right stage of your company. Solve a practical problem today, but then save the data and keep getting better at it,” explains Ross.

As an example, he recalls that one of the most challenging early tasks at ServiceTitan was creating quality contact lists for cold calling. So they implemented a simple solution: a telephone-based enrichment team, where new sales reps or business development representatives (BDRs) qualified leads by asking targeted questions in calls. This identified valuable accounts and filtered out unqualified ones.

Over time, this evolved into a data science operation using web scraping and cross-referencing to build a more accurate book of business.

You can enhance this process further by having your enrichment team research and qualify the list upfront, creating a high-quality prospect list to cold call. This saves your team time by eliminating leads from the start so you don’t have to call them again.

How ServiceTitan and LinkedIn integrate data into their processes 

ServiceTitan and LinkedIn are leveraging data in many interesting ways to improve their sales strategies and grow. Some of the use cases include:

To stay focused on their best customer profile

It’s important to focus on your ideal customer profile, even if your product appeals to other profiles. This allows you to allocate resources to where they’ll be most effective and build a strong base of loyal customers that will advocate for you, rather than attracting a broad, indifferent audience that doesn’t feel a strong connection to your product. This is what ServiceTitan is doing. 

Ross explains that they use data to identify ideal customers — those who are most likely to succeed with their product. Then, despite receiving high inbound demand, they turn down leads that don’t fit this ideal customer profile. “If we can’t guarantee long-term success for a customer, we’re better off not pursuing that prospect,” he says.

This discipline is difficult, especially because sales teams are often under pressure to meet targets and thus may be tempted to serve non-ideal customers. Tony understands this temptation and how one can fall into it. “We always sell to that person the first time, then we learn from it and avoid doing it again,” he says.

To stay disciplined like ServiceTitan, you’ll need to be confident your data is clean and accurate. This confidence comes from building a strong data foundation that aligns with the six data quality dimensions. “Scrub it and inspect it till you believe it,” says Tony.

To build predictive models from early internal data

Startup founders often focus on external data from third-party providers, but internal data is just as valuable. This includes insights into product usage patterns and changes in customer decision-makers. 

For example, user activity, such as logins and feature usage, can reveal engagement levels and areas where customers may need support. "This is especially important for SaaS companies, where renewals are critical. You need to track how you’re delivering value and what GTM actions (sales, marketing, post sales) are being taken based on the data,” Lekha explains.

Tony Rodoni was part of the early Salesforce team. While there, they studied customers’ trial usage data, such as who logs in or creates reports, to identify leads likely to convert. 

You can develop predictive models from your early data, such as one for forecasting churn and recommending actions if a user rarely logs in or doesn’t use key features.

To support outbound efforts

You can use data to enhance your outreach by identifying the best-fit prospects, personalizing messaging for them, and improving how you segment your audience.

Beyond prospecting, you can also use data to support other stages of the sales process, such as closing. It can help you pinpoint the best times to follow up with leads before the first account executive (AE) meeting and highlight key touchpoints with executive stakeholders to boost close rates.

These insights help teams act proactively and prevent issues that could jeopardize deals. “This creates a predictable engine,” Ross explains. “It allows us — and sales leaders across the organization — to track progress and understand the key drivers of success from our investments in data.”

To build and manage a scalable team

ServiceTitan and LinkedIn use data to hire better candidates and predict turnover. Ross explains that certain tools can create profiles of high-performing employees, and then identify candidates who match that profile during the hiring process, This increases the likelihood of a successful hire. 

Lekha’s team uses data to predict attrition and adjust hiring plans to stay fully staffed. Tools like LinkedIn Recruiter are used by talent acquisition teams to identify and nurture candidates likely to be open to new roles based on location, industry, and demographics.

If you’re in growth mode, plan for more hires than expected, as promotions or departures may require additional hiring. “If you're going from five people on a team to ten, you're not just hiring five new people," Ross explains. "You might need to hire seven, considering promotions and departures.”

To create compensation plans that drive growth

According to Lekha, the key levers for growth are new products and new markets. However, salespeople often prioritize familiar products and markets because they are easier to sell. 

To incentivize sales in tougher areas, companies often use compensation plans, and adjust these plans based on data. “When launching a new product at LinkedIn, we evaluate the sales cycle. If it's longer than usual, it typically means the product is harder to sell and requires more time and resources from the rep,” Lekha says. Creating a compensation plan that reflects the effort it’ll take from the rep helps build momentum and excitement within the organization. 

At times, LinkedIn ties incentives to new customer acquisition (logos) rather than just revenue and sets thresholds to ensure deal quality, especially when entering new markets. This incentivizes growth while avoiding low-value deals.

Ross advises two approaches to setting quotas: the easy way that uses benchmark reports, and the right way that aligns quotas with strategy and outcomes, considering factors like pipeline sources, conversion rates, cycle times, and contract values. “Everybody is familiar with financial plans that ramp up over the year, with a big push at the end of the quarter, especially in Q4. I prefer incentive plans that drive consistent pacing throughout the year. By staying on track month-to-date, week-to-date, and quarter-to-date, compensation can keep the team from falling behind and avoid last-minute rushes,” says Ross. These last-minute rushes create unpredictability, but Ross' recommended approach evenly distributes sales efforts throughout the year, helping salespeople enter Q4 closer to their target. 

[Pull quote: Instead of financial plans that increase over the year, with a big push in Q4, go for incentive plans that drive consistent pacing throughout the year to avoid last-minute rushes. - Ross Biestman, CRO at ServiceTitan]

Top metrics sales and GTM teams should track

Lekha groups these metrics into three categories: traditional sales funnel, product usage, and customer relationship metrics.

The traditional sales funnel metrics track the evolution and progression of leads through different stages of the sales pipeline. It measures individual sales rep performance and identifies drop-off points that hinder conversions.

Product usage metrics monitor how customers are using the product or service to assess engagement, adoption, and satisfaction. They can help identify potential upselling or cross-selling opportunities, and even churn signals.

Customer relationship metrics track customer renewals and spending during contract renewals. These metrics are indicators of customer satisfaction and business growth.

Additional metrics to track include customer acquisition cost, churn rate, customer lifetime value, sales velocity, number of qualified leads, sales cycle length, conversion rate, and net promoter score.

There’s just one caveat. Don’t monitor too many metrics, as it can cause data paralysis. Instead, have a North Star metric along with three to seven other relevant metrics that align with your company and GTM strategy. Tracking only the most important metrics helps uncover meaningful insights and prevents you from being overwhelmed. “It’s important to have a handful of core metrics and then drill down with one-off analyses as needed rather than having multiple dashboards,” explains Lekha. “Structure your analyses to draw key conclusions and, most importantly, take action based on what the data reveals.”

Ross concurs and adds that pipeline and funnel metrics should be part of what you track. “It's really important that you understand your funnel and be able to track specific data for those funnel metrics that are the most demonstrative drivers,” he says. In other words, these metrics are often more actionable because they give clear insights into areas like sales growth and conversion rates.

AI isn’t replacing salespeople, but it is enhancing sales operations

So far, AI hasn’t disrupted the core functions of sales because it lacks essential human skills like emotional intelligence, relationship building, and persuasion. However, it’s useful in preparatory tasks such as identifying high-priority leads and updating CRMs.

It also excels in complex areas like territory modeling, quota setting, and forecasting. Instead of distributing generic customer lists, it enables smarter territory modeling by analyzing multiple data sources to define ideal customer profiles. “AI can analyze data from various sources to create optimized territories,” says Lekha. Forecasting has advanced from basic pacing models to incorporating real-time signals like product usage, customer profiles, and macroeconomic trends — areas where AI adds significant value.

Lekha highlights AI's potential in coaching and feedback. By analyzing call recordings and behavioral data, AI could provide actionable insights to improve sales enablement and alignment. “AI could identify signals from call recordings to reveal what works, what doesn’t, and how reps perform against expectations,” she explains.

On the other hand, Ross observes that AI's future potential lies in automating the preparation and tailoring of outbound outreach. “You don't get a second chance at a first impression,” he says. “The criticality of the first interaction—whether it's a call or pitch — is important so you don’t waste the buyer’s time with irrelevant messaging. AI accelerates that process to deliver something tailored and valuable immediately," Ross concludes.

Before purchasing individual AI sales tools, check your tech stack. Many existing vendors already incorporate AI into their products, and you may find what you need without investing in new tools. "These AI capabilities are already embedded in your CRM, recording tools, and other platforms. You may already have more AI tools in your stack than you realize," Tony says.

Data-driven success starts with your first hire

Revenue operations are the fastest-growing job in America, a trend Lekha has noticed from her experience at LinkedIn. She says that operations roles—whether in revenue, sales, or GTM—are in high demand across startups and large organizations. 

This growth is no surprise, as modern companies depend on data and need skilled professionals to manage and leverage it. “It's really important to hire the right people that can synthesize the data to action,” says Lekha.

Building a data-driven sales team starts with the right first hire. Look for someone who is data-curious, data-driven, and adept at handling multiple tasks while establishing the processes and structure needed for your sales data team. They’ll lay the groundwork for improved decision-making and scalable growth through effective use of data.

For more actionable insights, check out the Go-to-Market Course we designed for SaaS companies of all stages. We share some best practices and insights to help leaders gain traction, position their businesses, and build recurring revenue engines. 

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